What are the properties of sound signals that induce the experience of groove in listeners? In particular, how do systematic patterns of signal properties (timing, metrical structure, loudness, etc.) relate to the experience of groove at several levels of the metrical structure?

To answer these questions, in ShakeIt, we follow an analysis/synthesis approach, and explore complementarities between three lines of work:

Automatic analysis of groove features from audio. In particular, focusing on automatic learning from examples of what we call the “groove archetype” of certain music styles.

Empirical experiments with human participants to validate/invalidate these features and to investigate if there are other relevant features for the perception of groove.

Implementation of a software for real-time generation/manipulation of polyphonic rhythmic sequences conveying the groove of a certain style, or to gradually change the groove feel of a rhythmic sequence at run time.